Recent years have witnessed the rapid growth of network-based mining studies. As a non-Euclidean data structure, the network-structure data (also known as graph data) widely exists in both real and virtual worlds, such as social network, knowledge graph (KG), protein molecular structure, brain neuron connectivity, and transportation topological graph. For human intelligence, it is extensively believed that graph plays a vital role in describing the connection of things, organizing data and knowledge, modeling problems, and decision-making processes. Therefore, how to smartly fuse heterogeneous data entities into a unified semantic network, and further utilize human expert knowledge to explain and even guide graph-based data mining is a key research question to the current artificial intelligence (AI) and data mining (DM) domain.

In the past few years, graph methods represented by deep graph learning have brought a new wave of upsurge to this problem from many aspects, and benefit areas that are recognized as human knowledge-dominated domains. In addition to the incredible achievements that have been made, scientists are still working towards revealing the nature of network mining methods and even the structural as well as semantic essence of different networks for these tasks.

With this objective in mind, we aim to invite research papers that center around knowledge-driven network fusion and mining. This will contribute to the establishment of a holistic framework encompassing the collection, processing, modeling, and application of heterogeneous network data. Moreover, we believe that this special session will not only enhance the understanding of methodology and engineering aspects but also foster practical applications in various industries. By doing so, we aspire to bridge the gap between academia and industry and facilitate knowledge-driven applications.

We encourage submissions on a range of topics, including but not limited to

  • Knowledge-driven graph fusion and mining theory:
    • Graph and network theory for data mining
    • Explainable graph-based neural networks and their mechanism
    • Graph representation for understanding and learning
    • Graph representation fusion for multiple neural networks
    • Propagation and spreading dynamics on complex networks
  • Methods of knowledge-driven graph fusion and mining with social, behavioral, and medical data:
    • Novel graph neural network architecture enhanced by knowledge
    • Multi-modal and multi-source heterogeneous graph fusion
    • Knowledge-guided graph-based reasoning and searching
    • Knowledge-empowered graph models for downstream tasks
    • Pretraining on graph
    • Few/Zero-shot learning on Graphs
  • Applications for social, behavioral, medical and other areas:
    • Domain knowledge-specific graph-based research
    • Large-scale industrial application of GNN and other graph methods with the help of knowledge
    • Research and application of GNN in human-computer interaction and humanities art such as intelligent simulation and digital creativity
  • New perspectives on knowledge-driven graph learning methods
    • Survey papers in knowledge-driven graph mining and its applications
    • Empirical studies of current methods on knowledge-driven learning
    • Discussion about limitations and challenges in this domain

Important Dates

  • Special Session Papers Submission: 01 May 2024
  • Acceptance Notification: 01 June 2024
  • Camera-Ready Submission: 15 June 2024
  • Author Registration: 18 June 2024
  • Conference Date: 16-18 August 2024

Paper submission instruction

Paper submission system is available at: https://easychair.org/conferences/?conf=besc2024.

All papers will be reviewed by the Program Committee on the basis of technical quality, relevance to BESC 2024, originality, significance and clarity.

Please note:

  • All submissions should use IEEE two-column style. Templates are available from here.
  • All papers must be submitted electronically through the paper submission system in PDF format only. BESC 2024 accepts special session papers (6 pages). The page count includes the references.
  • Paper review will be double-blind, and submissions not properly anonymized will be desk-rejected without review.
  • Submitted papers must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings.
  • Papers must be clearly submitted in English and will be selected based on their originality, timeliness, significance, relevance, and clarity of presentation.
  • Submission of a paper should be regarded as a commitment that, should the paper be accepted, at least one of the authors will register and attend the conference to present the work.
  • Accepted papers will be submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and quality requirements and indexed by EI. Top quality papers after presented in the conference will be selected for extension and publication in several special issues of international journals, e.g., World Wide Web Journal (Springer), Web Intelligence (IOS Press), Social Network Analysis and Mining (Springer), Human-Centric Intelligent Systems (Springer), Natural Language Processing (Elsevier), etc.
  • The use of artificial intelligence (AI)–generated text in an article shall be disclosed in the acknowledgements section of any paper submitted to an IEEE Conference or Periodical. The sections of the paper that use AI-generated text shall have a citation to the AI system used to generate the text.

Organizing Committee

Program Chair
Yifan Zhu (Email: yifan_zhu[at]bupt.edu.cn), Beijing University of Posts and Telecommunications, China.
Kaize Shi, University of Technology Sydney, Australia
Qika Lin, National University of Singapore, Singapore

Program Committee Members
Qi Zhang, Tongji University, China
Xuesong Li, Beijing Institute of Technology, China
Fuquan Zhang, Minjiang University, China
Yang Li-ao Geng, Beijing Jiaotong University, China
Wei Fan, Oxford University, United Kingdom
James Chambua, University of Dar es Salaam, Tanzania
Hao Lu, Institute of Automation Chinese Academy of Sciences, China
Kai He, National University of Singapore, Singapore
Yuandong Wang, Tsinghua Univeristy, China
Guijin Li, University of Toronto, Canada